Explaining foreign direct investment patterns in Central and East Europe: A neural network approach

Quantitatively examining the determinants of foreign direct investment (FDI) in Central and East Europe (CEE) is an important research area. Traditional linear regression approaches have had difficulty in achieving conceptually and statistically reliable results. In this paper, we offer a novel approach to examining FDI in the CEE region. The key tasks addressed in this research are (i) a neural network based FDI forecasting model and (ii) nonlinear evaluation of the determinants of FDI. The methodology is nontraditional for this kind of research. In terms of MSE and R-squared criteria, we find that NN approaches better able to explain FDI determinants' weights than traditional regression methodologies. Our findings are preliminary but offer important and novel implications for future research in this area including more detailed comparisons across sectors as well as countries over time.